Exposure to Neighborhood Walkability and Residential Greenness and Incident Fracture

This cohort study evaluates the association between incident fracture and exposure to neighborhood walkability and residential greenness.

This supplemental material has been provided by the authors to give readers additional information about their work.
Several studies have demonstrated the validation of Walk Score for estimating neighborhood walkability 1,2 .Based on the Walk Score framework, Su and colleagues proposed a modified walkability calculation method suitable for neighborhoods in China 3,4 .Details of the methodology have been elaborated in previous studies 3,4 .In brief, the assessment process was mainly carried out in four steps: 1. Point of interest (POI) selection and weights determination.We selected 6 principle amenities (19 items) and their weight from Su's study 3 .Then, we identified amenities in July 2015 around each participant's neighborhood via road network files through Gaode Map (https://lbs.amap.com/api/,accessed August 26, 2021).
2. Travel time calculation and decay function determination.Considering the actual road network and traffic condition, we used Gaode Map to calculate the travel time from residence address to each POI.We adopted decay function to reflect that residents' interest in POI would gradually decay with the increase of travel time.We utilized the tolerance time approach, as demonstrated in Walk Score (https://www.walkscore.com/), to estimate the decay function.
Combining the decay function and the travel time, we obtained the adjusted weight of POI.The decay function was demonstrated as follows: 4. Standardization of walk score.Walk score was standardized into the interval between 0 and 100, which was assigned to each participant according to the address.The standardization formula was presented as follows: Standardized walk score = Adjusted walk score − min (adjusted walks core) max(adjusted walk score) − min (adjusted walk score)

Exposure: Residential Greenness
Exposure to residential greenness was assessed by satellite-derived Normalized Difference Vegetation Index (NDVI).NDVI, an indicator of greenness quality and intensity, was measured based on differential spectral signatures of chlorophyll in the visible (red band) and near infrared regions 5 .NDVI was calculated as the ratio of the difference of near-infrared region and red reflectance to the sum of these two measures 6 .
The NDVI values ranged from -1 to 1, with higher values indicating a higher level of greenness.Data on greenness were obtained from the moderate resolution imaging spectroradiometer (MODIS) (https://modis.gsfc.nasa.gov/data/dataprod/mod13.php) carried in Terra and Aqua satellites launched by the National Aeronautics and Space Administration (NASA) 7,8 .Imageries on greenness with a spatial resolution were provided by MODIS for every 16 days.We calculated NDVI based on the same days of the year (days 001, 017, 097, 113, 193, 209, 257, 289, and 305) to represent the four seasons 9 .We linked the imageries to the residential address of each participant and NDVI was calculated in radii of 1000m around residential address based on ArcGIS.In this study, one-year average NDVI before the baseline were calculated as an indicator of residential greenness.

Covariates
All covariates were measured at baseline, including: age at baseline (in years), gender (males or females), body mass index (BMI), education (illiterate, primary or middle school, and high school or above), occupation (industry or agriculture, enterprise or public institution, housework or retirement, and others), household income (<10,000, 10,000-29,999, 30,000-49,999, and ≥50,000 Chinese Yuan (CNY) per year), history of osteoporosis (yes or no), history of diabetes (yes or no) and concentration of PM2.5.
Demographic information and lifestyle factors were obtained using standardized questionnaires by trained healthcare staff at baseline.Age at baseline was calculated as the time interval between date of birthday and baseline.We categorized participants into three 3 groups: underweight (BMI <18.5 kg/m2), normal weight (18.5 ≤ BMI < 24.0 kg/m 2 ) and overweight or obesity (24.0 kg/m 2 ≤ BMI) according to the guidelines for prevention and control of overweight and obesity in Chinese adults 10 .History of osteoporosis or diabetes was defined using ICD-10 codes (E10-E14; M80-M82) based on medical records through the YHIS at baseline.We calculated 1-year average concentrations of PM2.5 by land-use regression model before the baseline for each participant 11 .

eFigure 1 . 2 . 3 .
Joint Hazard Ratios for Associations of Decreased Neighborhood Walkability and Decreased Residential Greenness With Incident of Fracture.Abbreviation: NDVI, normalized difference vegetation index; BMI, body mass index; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 μm.The joint hazard ratios (JHR) were represented per IQR decrease in walkability and IQR decrease in NDVI relative to no decrease in either of two exposures.IQR for walkability: 34.18 , NDVI: 0.13.Adjusted for age (timescale), gender, household income, education, occupation, BMI, history of osteoporosis, history of diabetes and concentration of PM2.5.NDVI was measured at a buffer of 1000 m. eFigure Contour Plot for the Interaction Effect of Walkability and Greenness on Incident Fracture.Abbreviation: NDVI, normalized difference vegetation index; BMI, body mass index; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 μm.Adjusted for age (timescale), gender, household income, education, occupation, BMI, history of osteoporosis, history of diabetes and concentration of PM2.5.NDVI was measured in buffer of 1000 m. eFigure Associations Between Quartiles of Greenness and Incident Fracture by Walkability Quartiles.Abbreviation: HR, hazard ratio; CI, confidence interval; Q, quartile; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 μm; BMI, body mass index; NDVI, normalized difference vegetation index.Adjusted for age (timescale), sex, household income, education, occupation, BMI, history of osteoporosis, history of diabetes, concentration of PM2.5.Greenness was measured using NDVI in buffer of 1000m.
Joint Hazard Ratios for Associations of Decreased Neighborhood Walkability and Decreased Residential Greenness With Incident of Fracture eFigure 2. Contour Plot for the Interaction Effect of Walkability and Greenness on Incident Fracture eFigure 3. Associations Between Quartiles of Greenness and Incident Fracture by Walkability Quartiles eTable 1. Number of Fractures by Different Sites and ICD-10 Codes eTable 2. Number of Participants With Missing Data for Each Variable of Interest eTable 3. Sensitivity Analyses by Using Multiple Imputation Chained Equations to Impute Missing Values of Covariates eTable 4. Sensitivity Analyses by Excluding Participants Who had Relocated Within 10 Years Before Baseline eTable 5. Sensitivity Analyses by Altering the End Point of Follow-Up to December 31, 2019 eTable 6. Sensitivity Analyses by Including Pathologic Fractures, Stress Fractures and Injury Fractures eTable 7. Sensitivity Analyses by Excluding Participants Who Received a Rheumatoid Arthritis Diagnosis at Baseline eReferences
Abbreviations: BMI, body mass index.a Participants who were lost to follow-up and those with unidentified addresses or ID or duplicated records were excluded.b Values were presented as number (percentages). a